Artificial research participants in behavioral science

Purpose The potential for large language models (LLMs) to improve behavioral science research has generated significant discussion. But, the specific role that LLMs should serve in behavioral research, especially in terms of simulating human participants, remains an open research question. The purpose of this work is to engage with this open question and address a critical gap in the literature stemming from the lack of a practical framework for realistically using artificial research participants. Design/methodology/approach Google Scholar was systematically searched for modern, peer-reviewed literature. Additional articles were found by both backward and forward citation searching the relevant articles. Exclusion criteria were articles that were not directly related to artificial intelligence (AI) and/or research participants, and articles written in a language other than English. This approach resulted in 26 citations that comprehensively capture current perspectives. Findings This study proposes two novel stances: that artificial research participants can complement human participants during data collection, and replace human participants during pilot testing. This framework engages with the open question of artificial research participants usage while addressing a framework gap in the literature. Originality/value This workadvances discourse LLMs potentially transforming behavioral science by establishing a framework differentiating the use of artificial research participants in data collection versus pilot testing. This study reinforces this framework with clear implementation guidelines that maximize the strengths of AI while respecting the human element and the methodological integrity of behavioral research.